Control Engineering Inspired Design Tools for Synthetic Biology
Lead Research Organisation:
Imperial College London
Department Name: Bioengineering
Abstract
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Organisations
Publications
Amor B
(2014)
Uncovering allosteric pathways in caspase-1 using Markov transient analysis and multiscale community detection.
in Molecular bioSystems
Amor BR
(2016)
Prediction of allosteric sites and mediating interactions through bond-to-bond propensities.
in Nature communications
Arpino JAJ
(2013)
Tuning the dials of Synthetic Biology.
in Microbiology (Reading, England)
Beguerisse-Díaz M
(2018)
Flux-dependent graphs for metabolic networks.
in NPJ systems biology and applications
Beguerisse-Díaz M
(2016)
Linear models of activation cascades: analytical solutions and coarse-graining of delayed signal transduction.
in Journal of the Royal Society, Interface
Beguerisse-Díaz M
(2012)
Squeeze-and-breathe evolutionary Monte Carlo optimization with local search acceleration and its application to parameter fitting.
in Journal of the Royal Society, Interface
Beguerisse-Díaz M
(2014)
Interest communities and flow roles in directed networks: the Twitter network of the UK riots.
in Journal of the Royal Society, Interface
Billeh Y
(2014)
Revealing cell assemblies at multiple levels of granularity
in Journal of Neuroscience Methods
| Description | * A series of mathematical methods for the analysis of data from experiments in Systems and Synthetic Biology including: ** protocols for the generation of models in Synthetic Biology in iteration with data generation (elimination of variables and parameter fitting) ** algorithms for data fitting of time series using evolutionary Monte Carlo methods ** graph-theoretical algorithms for the analysis of flows on networks, including directed networks ** finding roles in directed networks of metabolites with applications to social networks ** application to a reformulation of metabolic networks using flux graphs in collaboration with Polytechnic University of Valencia and Oxford University ** data analysis tools for model reduction of synthetic genetic circuits applied directly to data in an iterative cycle, in collaboration with Oxford and Luxembourg ** time series data analysis has been applied to social network data and online behaviours (i.e., website usage in education). |
| Exploitation Route | The methods have been taken up in different directions including: * the analysis of Twitter data by several companies and public bodies, * the analysis of anomalies in time series data from the finance industry, * the analysis of allosteric sites in proteins in collaboration with the pharmaceutical industry * the analysis of metabolic networks using Flux Balance Analysis models |
| Sectors | Agriculture Food and Drink Communities and Social Services/Policy Creative Economy Digital/Communication/Information Technologies (including Software) Healthcare Government Democracy and Justice Manufacturing including Industrial Biotechology Pharmaceuticals and Medical Biotechnology |
| Description | The results in this work have been applied to collaborations with Syngenta leading to the development of methods for the analysis of toxicological data collected by the company to assess the potential carcinogenic effect of compounds being developed as potential herbicides before they are released to the environment. The work was followed up with funding by Syngenta for a pilot project funding an RA and through an EU-funded PhD studentship under the AgriNet initiative. In addition, the work has also led to unexpected connections with other data types, including time series in social media, patient trajectories in healthcare, web usage of online course by students, network analytics of social networks, and analysis of text documents. This has led to ongoing collaborations with Spotify, LayerIV (companies in data science) as well as collaborations with NHS Trusts. |
| First Year Of Impact | 2013 |
| Sector | Agriculture, Food and Drink,Communities and Social Services/Policy,Creative Economy,Digital/Communication/Information Technologies (including Software),Healthcare,Government, Democracy and Justice,Pharmaceuticals and Medical Biotechnology |
| Impact Types | Societal Economic |
| Description | Syngenta collaborative grant |
| Amount | £70,000 (GBP) |
| Organisation | Syngenta International AG |
| Department | Syngenta Ltd (Bracknell) |
| Sector | Private |
| Country | United Kingdom |
| Start | 08/2013 |
| End | 11/2014 |
| Description | Organisation of the international Workshop on "Control Engineering and Synthetic Biology", University of Oxford, Oxford, 10-12 September 2014 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | Over 100 fellow researchers (professors, postdocs, graduate students) attended the workshop that Dr Stan co-organised at the University of Oxford. Furthermore, Dr Jordan Ang gave an talk describing the EPSRC funded project EP/K020617/1. Many requests for additional discussions. |
| Year(s) Of Engagement Activity | 2014 |
| URL | http://sysos.eng.ox.ac.uk/wiki/index.php/Workshop_on_Control_Engineering_and_Synthetic_Biology |